a j*@sddlZddlmZddlZddlmZddlmZddl m Z m Z m Z ddl mZddlmZdd lmZmZdd lmZmZdd lmZmZd d lmZGdddee e ZdS)N)Real)sparse)linprog) BaseEstimatorRegressorMixin _fit_context)ConvergenceWarning)_safe_indexing)Interval StrOptions) parse_version sp_version)_check_sample_weight validate_data) LinearModelcseZdZUdZeeddddgeeddddgdgehd gedgd Zee d <d d dddd ddZ e dddddZ fddZ ZS)QuantileRegressora Linear regression model that predicts conditional quantiles. The linear :class:`QuantileRegressor` optimizes the pinball loss for a desired `quantile` and is robust to outliers. This model uses an L1 regularization like :class:`~sklearn.linear_model.Lasso`. Read more in the :ref:`User Guide `. .. versionadded:: 1.0 Parameters ---------- quantile : float, default=0.5 The quantile that the model tries to predict. It must be strictly between 0 and 1. If 0.5 (default), the model predicts the 50% quantile, i.e. the median. alpha : float, default=1.0 Regularization constant that multiplies the L1 penalty term. fit_intercept : bool, default=True Whether or not to fit the intercept. solver : {'highs-ds', 'highs-ipm', 'highs', 'interior-point', 'revised simplex'}, default='highs' Method used by :func:`scipy.optimize.linprog` to solve the linear programming formulation. It is recommended to use the highs methods because they are the fastest ones. Solvers "highs-ds", "highs-ipm" and "highs" support sparse input data and, in fact, always convert to sparse csc. From `scipy>=1.11.0`, "interior-point" is not available anymore. .. versionchanged:: 1.4 The default of `solver` changed to `"highs"` in version 1.4. solver_options : dict, default=None Additional parameters passed to :func:`scipy.optimize.linprog` as options. If `None` and if `solver='interior-point'`, then `{"lstsq": True}` is passed to :func:`scipy.optimize.linprog` for the sake of stability. Attributes ---------- coef_ : array of shape (n_features,) Estimated coefficients for the features. intercept_ : float The intercept of the model, aka bias term. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 n_iter_ : int The actual number of iterations performed by the solver. See Also -------- Lasso : The Lasso is a linear model that estimates sparse coefficients with l1 regularization. HuberRegressor : Linear regression model that is robust to outliers. Examples -------- >>> from sklearn.linear_model import QuantileRegressor >>> import numpy as np >>> n_samples, n_features = 10, 2 >>> rng = np.random.RandomState(0) >>> y = rng.randn(n_samples) >>> X = rng.randn(n_samples, n_features) >>> # the two following lines are optional in practice >>> from sklearn.utils.fixes import sp_version, parse_version >>> reg = QuantileRegressor(quantile=0.8).fit(X, y) >>> np.mean(y <= reg.predict(X)) np.float64(0.8) rrZneither)closedNleftboolean> highs-ipmzrevised simplexhighs-dsinterior-pointhighsquantilealpha fit_interceptsolversolver_options_parameter_constraintsg?g?TrcCs"||_||_||_||_||_dS)Nr)selfrrrrr r#DE:\soccer\ml_env\Lib\site-packages\sklearn/linear_model/_quantile.py__init__s zQuantileRegressor.__init__)Zprefer_skip_nested_validationcCst|||gdddd\}}t||}|jd}|}|jrB|d7}t||j}|jdkrztt dkrzt d|jd t |r|jd vrt d|jd |j d ur|jdkrd di}n|j }t|d}t|} | t|kr||}t||}t||}ttjd||d||j|d|jg} |jrJd| d<d| |<|jd vrt j| |jdd} |jrt tj| df|jd} t j| || | | | gdd} nt j|| | | gdd} n\t| } |jr t| df} tj| || | | | gdd} ntj|| | | gdd} |}t| | ||j|d}|j}|jsddddd}td|jd||jddd |j t!|d |||d|}|j"|_#|jr|dd |_$|d|_%n ||_$d!|_%|S)"aFit the model according to the given training data. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training data. y : array-like of shape (n_samples,) Target values. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- self : object Returns self. )cscZcsrZcooTF)Z accept_sparseZ y_numericZ multi_outputrrz1.11.0zSolver z- is not anymore available in SciPy >= 1.11.0.)rrrz; does not support sparse X. Use solver 'highs' for example.NZlstsqrr)Z fill_valuer&)dtypeformat)shaper')r()Zaxis)cA_eqb_eqmethodoptionszIteration limit reached.z!Problem appears to be infeasible.z Problem appears to be unbounded.z#Numerical difficulties encountered.)rrzDLinear programming for QuantileRegressor did not succeed. Status is z: zunknown reason zResult message of linprog: g)&rrr)rnpsumrrrr ValueErrorrissparser Znonzerolenr Z concatenatefullreyer'Z csc_matrixonesZhstackrxsuccesswarningswarnstatus setdefaultmessager nitZn_iter_Zcoef_Z intercept_)r"XyZ sample_weightZ n_featuresZn_paramsrr indicesZ n_indicesr*r8r9r+r,resultZsolutionZfailureparamsr#r#r$fits          " "   zQuantileRegressor.fitcst}d|j_|S)NT)super__sklearn_tags__Z input_tagsr)r"tags __class__r#r$rI*s z"QuantileRegressor.__sklearn_tags__)N)__name__ __module__ __qualname____doc__r rr dictr!__annotations__r%rrGrI __classcell__r#r#rKr$rs( Y   r)r<numbersrnumpyr2ZscipyrZscipy.optimizerbaserrr exceptionsr utilsr Zutils._param_validationr r Z utils.fixesr rZutils.validationrrZ_baserrr#r#r#r$s